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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yurie Kondo Shima Okada Moemi Saito Takahide Tanaka Kanaru Fumimoto Graduate School of Science and Engineering Ritsumeikan University Shiga Japan Department of Robotics Faculty of Science and Engineering Ritsumeikan University Shiga Japan Corp. Value Link Technology Tokyo Japan

ISBN: (纸本)9781665492331

The COVID-19 pandemic caused by a virus called RARS-CoV-2 spread worldwide. As the result, the number of people who are lack exercise has increased. In addition, there are few systems to evaluate exercise movements and facilitate physical exercise at home. In this study, we developed a boxing glove type sensation device an acceleration sensor was installed in a boxing glove to evaluate the power of a punch, and the device has the function of glitz to the punches by producing sound, vibration, and light according to the type and power of the punching motion. An evaluation experiment was conducted to confirm that this device could induce exercise and promote health by having fun. A comparison was made between a normal boxing glove and the boxing glove type sensation device. As the results, we could confirm that, the number of strong punches with high acceleration and the total number of punches increased in the sensation device. In addition, the heart rate after exercise increased significantly compared to before and after exercise. The results also showed an increase in mood elevation and positive emotions.

关键词： Vibrations Heart rate COVID-19 Visualization Pandemics Mood Punching

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Development of a Software for Laser-Based Micromachining of Piezoceramic Microactuators 1

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AUN/SEED-Net Joint Regional Conference in Transportation, Energy, and Mechanical Manufacturing Engineering, RCTEMME 2021

作者： Nguyen, Ha Xuan Nguyen, Hung Anh Department of Machine Design and Robotics School of Mechanical Engineering Hanoi University of Science and Technology No. 1 Dai Co Viet Hai Ba Trung Hanoi Viet Nam Department of Mechatronics Engineering Faculty of Mechanical Engineering Thuy Loi University No. 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (数字)9789811919688

ISBN: (纸本)9789811919671

Laser-based micromachining is often applied to the fabrication of piezoceramic microactuators. In fact, the manual generation of a G-code file for the micromachining of such microactuators is very hard work. In this work, a CAM software for the laser-based micromachining of piezoceramic microactuators was developed. The software was built using C++ and the OpenCV library with Microsoft Visual Studio 9.0. The software has three main classes, including ReadDXF, DrawDXF, and MakeGcode. ReadDXF and DrawDXF were developed to read and display DXF data of the design of fabricated parts in an image for further processing. MakeGcode was built for generating G-code, which is then included in the main program of the NView for running the machine. Robust algorithms were developed so that the software could read complex designs without any failure. Optimization of the toolpath, which can improve machine productivity and lifetime service, was introduced. Results and experimental implementations show that our software can efficiently be used to machine piezoceramic microactuators. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Laser machining Microactuators Piezoceramic

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Sustainable AI: Mathematical Foundations of Spiking Neural Networks

arXiv

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arXiv 2025年

作者： Fono, Adalbert Singh, Manjot Araya, Ernesto Petersen, Philipp C. Boche, Holger Kutyniok, Gitta Department of Mathematics Ludwig-Maximilians-Universität München Germany Faculty of Mathematics and Research Network Data Science University of Vienna Austria Institute of Theoretical Information Technology Technical University of Munich Germany Munich Germany Department of Physics and Technology University of Tromsø Tromsø Norway Institute of Robotics and Mechatronics DLR-German Aerospace Center Germany

Deep learning’s success comes with growing energy demands, raising concerns about the long-term sustainability of the field. Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains. This article examines the computational properties of spiking networks through the lens of learning theory, focusing on expressivity, training, and generalization, as well as energy-efficient implementations while comparing them to artificial neural networks. By categorizing spiking models based on time representation and information encoding, we highlight their strengths, challenges, and potential as an alternative computational paradigm. Copyright © 2025, The Authors. All rights reserved.

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Exploring the Potential of LLM-based Chatbots for Task Scheduling in Robot Operations

Exploring the Potential of LLM-based Chatbots for Task Sched...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Catarina Rema Armando Sousa Heber Sobreira Pedro Costa Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

The rise of Industry 4.0 has revolutionized manufacturing by integrating real-time data analysis, artificial intelligence (AI), automation, and interconnected systems, enabling adaptive and resilient smart factories. Autonomous Mobile Robots (AMRs), with their advanced mobility and navigation capabilities, are a pillar of this transformation. However, their deployment in job shop environments adds complexity to the already challenging Job Shop Scheduling Problem (JSSP), expanding it to include task allocation, robot scheduling, and travel time optimization, creating a multi-faceted, non-deterministic polynomial-time hardness (NP-hard) problem. Traditional approaches such as heuristics, meta-heuristics, and mixed integer linear programming (MILP) are commonly used. Recent AI advancements, particularly large language models (LLM), have shown potential in addressing these scheduling challenges due to significant improvements in reasoning and decision-making from textual data. This paper examines the application of LLM to tackle scheduling complexities in smart job shops with mobile robots. Guided by tailored prompts inserted manually, LLM are employed to generate scheduling solutions, being these compared to an heuristic-based method. The results indicate that LLM currently have limitations in solving complex combinatorial problems, such as task scheduling with mobile robots. Due to issues with consistency and repeatability, they are not yet reliable enough for practical implementation in industrial environments. However, they offer a promising foundation for augmenting traditional approaches in the future.

关键词： Job shop scheduling Service robots Large language models Chatbots Real-time systems Mobile robots Resource management Reliability Autonomous robots Smart manufacturing

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A Deep Learning Approach with Uncertainty Estimation to Assess Aboveground Biomass Mapping of Tropical Rainforest in Thailand

A Deep Learning Approach with Uncertainty Estimation to Asse...

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Geoscience and Remote Sensing Symposium (M2GARSS), Mediterranean and Middle-East

作者： Nattawach Sataudom Songphon Reangsang Suwit Navakam Poramate Manoonpong Anuphao Aobpaet Pasuta Sunthornhao Natnicha Yooyen Mattana Pongsopon Natthasiree Chulinrak Sasawat Soontaros Panunya Charoensawadpong Soravis Supavetch Varuna Company Limited Bangkok Thailand Reforestation and Ecology Institute PTT Public Company Limited Bangkok Thailand Bio-Inspired Robotics and Neural Engineering Vidyasirimedhi Institute of Science and Technology Rayong Thailand Department of Civil Engineering Faculty of Engineering Kasetsart University Bangkok Thailand Department of Forest Management Faculty of Forestry Kasetsart University Bangkok Thailand

ISBN: (数字)9798350358582

ISBN: (纸本)9798350358599

This study proposes an aboveground biomass (AGB) density model to overcome the limitation of sparse data coverage in Thailand and scale at national level. The model is based on a U-Net convolutional neural network and fuses spaceborne remote sensing data including Sentinel-1, Sentinel-2, and GEDI. Five models were developed using a deep ensemble technique to provide uncertainty estimation, where accuracy was validated using three types of forest inventory data. Tropical rainforests and other forested areas such as perennial crop regions with similar geographical characteristics were evaluated. Results show high uncertainty in the out-of-distribution data areas, with %RRMSE for Pará rubber, mixed deciduous forest, and dry evergreen forest of 17.05, 24.47, and 14.0, respectively.

关键词： Rainforests Uncertainty Fuses Biological system modeling Estimation Forestry Data models

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Amino-functionalized reduced graphene oxide@MoS2@CoS2 heterostructure as a high-rate anode material for ultralong lifespan sodium–sulfur batteries

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New Journal of Chemistry 2025年第24期49卷 10379-10387页

作者： Navid Aslfattahi Maryam Sadat Kiai Nilgun Baydogan Lingenthiran Samylingam Kumaran Kadirgama Chee Kuang Kok Department of Fluid Dynamics and Thermodynamics Faculty of Mechanical Engineering Czech Technical University in Prague Prague 166 07 Czech Republic Nano-Science and Nano-Engineering Program Graduate School of Science Engineering and Technology Istanbul Technical University Istanbul-34469 Turkey Energy Institute Ayazaga Campus Istanbul Technical University Maslak Istanbul Turkey Centre for Advanced Mechanical and Green Technology Center of Excellence for Robotics & Sensing Technologies Faculty of Engineering and Technology Multimedia University Jalan Ayer Keroh Lama Bukit Beruang 75450 Melaka Malaysia Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang 26600 Pekan Malaysia Centre for Research in Advanced Fluid and Process University Malaysia Pahang Al-Sultan Abdullah Lebuhraya Tun Razak Gambang Kuantan Pahang Malaysia Centre for Sustainable Materials & Surface Metamorphosis (CSMSM) Chennai Institute of Technology Chennai India

A nanocomposite comprising MoS2@CoS2 amino-functionalized graphene (En-RGO) with a two-dimensional structure has been successfully synthesized using a simple self-assembly technique. The resulting cell with a MoS2@CoS...

A nanocomposite comprising MoS₂@CoS₂ amino-functionalized graphene (En-RGO) with a two-dimensional structure has been successfully synthesized using a simple self-assembly technique. The resulting cell with a MoS₂@CoS₂@En-RGO anode and a sulfur cathode exhibits a superior hierarchical architecture that significantly facilitates ion mobility, cell conductivity, and pseudocapacitance. Consequently, this leads to improved rate performance and extended cycle stability in Na–S batteries. Micron-sized CoS₂ was synthesized by solvothermal growth of MoS₂ nanosheets on its surface. This composite material presents enhanced sodium-ion diffusion kinetics attributed to its heterogeneous interface, reduced ion diffusion pathways and increased exposure of active sites for sodium storage due to its hierarchical nanosheet structure. Additionally, amino functionalized graphene oxide with uniform nitrogen doping could improve electronic conductivity. The cell employing MoS₂@CoS₂@En-RGO with superior ion mobility and enhanced adsorption energy of Na⁺ ions achieves a cycle stability of 541.2 mA h g⁻¹ after 1800 cycles. The increased adsorption energy facilitates a more efficient sodiation process at MoS₂@CoS₂@En-RGO compared to En-RGO. Additionally, the MoS₂@CoS₂@En-RGO anode demonstrates a capacity retention of 95.8% after 1800 cycles at 1 A g⁻¹. The enhanced mobility of Na ions and the effective adsorption of polysulfide chains in MoS₂@CoS₂@En-RGO have been examined through DFT calculations, confirming its potential as a promising anode material for

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Evaluation of Continual Learning Methods for Object Hardness Recognition

Evaluation of Continual Learning Methods for Object Hardness...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yiwen Liu Senlin Fang Chengliang Liu Jingnan Wang Ke Mai Yupo Zhang Zhengkun Yi Xinyu Wu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Faculty of Data Science City University of Macau Macua China PR

How to solve the problem of continual learning of robotic tactile perception in an open, dynamic environment is a pressing task. Although continual learning has been extensively studied in computer vision, the evaluation of those algorithms does not provide insights on whether their solutions may help continual learning in the context of tactile robotics. In this paper, we propose a continual learning scenario in the robotic tactile domain, and design experiments with tactile hardness dataset that allow us to compare the performance of many continual learning algorithms in the tactile domain. We examine the performance of regularization-based and replay methods in the tactile domain and discuss the effect of replay number on model performance. We show that regularization-based methods do not perform well on the tactile dataset. On the contrary, replay methods can maintain better anti-forgetting performance, but the number of model parameters will be larger, and the training time will be longer. We also found that the replay ratio should be higher than 0.2 to maintain the performance of the replay method on the tactile hardness dataset.

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Impact Evaluation of Driving Style on Electric Vehicle Battery based on Field Testing Result

Impact Evaluation of Driving Style on Electric Vehicle Batte...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Ka Seng Chou Davide Aguiari Rita Tse Su-Kit Tang Giovanni Pau Faculty of Applied Sciences Macao Polytechnic University Macao SAR China Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna Bologna IT Autonomous Robotics Research Center Technology Innovation Institute UCLA Samueli Computer Science University of California Los Angeles

Monitoring electric vehicles' battery status and forecasting their state of health is still an open challenge. To determine how and why a battery degrades over time, we have extensively monitored a Nissan Leaf's battery pack for more than one year. Collecting more than 4.5 million samples via a custom monitoring connected device to investigate how different driving behaviors affect battery aging. In addition, the best driving behaviors based on the battery's optimal temperature are revealed, including speed, acceleration and brake pedal pressure, and horsepower.

关键词： Temperature sensors Temperature measurement Aging Electric vehicles Batteries Behavioral sciences Brakes

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Enhancing Thermal Properties: A Comprehensive Study on Cellulose Nanocrystal Nanofluids 2nd

Enhancing Thermal Properties: A Comprehensive Study on Cellu...

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2nd International Conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Kareem Ahmad Aqeel, Ahmad Abdul Hajjaj, Sami Mohamed, Hassan Gomaa, Mohamed R. Alsboul, Monther Selangor Kajang43000 Malaysia School of Robotics Xi’an Jiaotong-Liverpool University 111 Taicang Avenue Taicang Jiangsu Province Suzhou215400 China School of Engineering and Technology Sunway University Selangor Darul Ehsan No. 5 Jalan Universiti Bandar Sunway47500 Malaysia Selangor Kajang43000 Malaysia Mechanical Engineering Department Faculty of Engineering Al-Hussein Bin Talal University Ma’an71111 Jordan Physics Department College of Science Al Hussein Bin Talal University Maan71111 Jordan

ISBN: (纸本)9789819639489

Nanofluids, characterized by a base fluid infused with nanoparticles, have gained substantial interest due to their superior thermal properties, primarily enhanced heat transfer capabilities. This study investigates the dispersion of cellulose nanocrystal (CNC) nanoparticles in base fluid and its subsequent impact on thermal properties. CNC nanoparticles obtained from Blue Goose Biorefineries Inc. Were dispersed in distilled water and ethylene glycol (EG) at varying volume concentrations (0.25%, 0.50%, 0.75%). The prepared nanofluids underwent thorough stability analyses utilizing techniques such as ultrasonication. The CNC nanofluids ‘ thermal conductivity, density, viscosity, and specific heat capacity were systematically measured using instrumentation. The study found that adding cellulose nanocrystal (CNC) nanoparticles to an 80:20 water-ethylene glycol mixture significantly improves the thermal conductivity, viscosity, and density of the base fluid. Thermal conductivity increased linearly with higher CNC concentrations (0.25%, 0.5%, 0.75%) and temperatures (25–55 ℃), peaking at 0.815 W/m·K at 55 ℃ with 0.75% CNC. Viscosity decreased with temperature but increased with higher CNC concentrations. Density also increased with CNC concentration and decreased with temperature, while specific heat capacity showed minimal changes with concentration but a slight increase with temperature. These enhancements make CNC nanofluids beneficial for heat transfer applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Viscosity

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RL-Based Guidance in Outpatient Hysteroscopy Training: A Feasibility Study

RL-Based Guidance in Outpatient Hysteroscopy Training: A Fea...

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IEEE International Conference on robotics and Biomimetics

作者： Vladimir Poliakov Kenan Niu Emmanuel Vander Poorten Dzmitry Tsetserukou Intelligent Space Robotics Lab Skolkovo Institute of Science and Technology Moscow Russia Faculty of Mechanical Engineering KU Leuven Robot Assisted Surgery group Leuven Belgium

ISBN: (纸本)9781665481106

This work presents an RL-based agent for out-patient hysteroscopy training. Hysteroscopy is a gynecological procedure for examination of the uterine cavity. Recent ad-vancements enabled performing this type of intervention in the outpatient setup without anaesthesia. While being beneficial to the patient, this approach introduces new challenges for clinicians, who should take additional measures to maintain the level of patient comfort and prevent tissue damage. Our prior work has presented a platform for hysteroscopic training with the focus on the passage of the cervical canal. With this work, we aim to extend the functionality of the platform by designing a subsystem that autonomously performs the task of the passage of the cervical canal. This feature can later be used as a virtual instructor to provide educational cues for trainees and assess their performance. The developed algorithm is based on the soft actor critic approach to smooth the learning curve of the agent and ensure uniform exploration of the workspace. The designed algorithm was tested against the performance of five clinicians. Overall, the algorithm demonstrated high efficiency and reliability, succeeding in 98 % of trials and outperforming the expert group in three out of four measured metrics.

关键词： Training Measurement Irrigation Tissue damage Instruments Force feedback Force

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